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Related Concept Videos

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Uncertainty: Overview00:59

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Random and Systematic Errors01:20

Random and Systematic Errors

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Systematic Error: Methodological and Sampling Errors01:15

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Types of Errors: Detection and Minimization01:12

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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Updated: Mar 8, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Measuring Analytical Quality: Total Analytical Error Versus Measurement Uncertainty.

James O Westgard1, Sten A Westgard2

  • 1Department of Pathology and Laboratory Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI 53705, USA; Westgard QC, Inc, Madison, WI 53717, USA.

Clinics in Laboratory Medicine
|February 4, 2017
PubMed
Summary

Total Analytical Error (TAE) and Measurement Uncertainty (MU) are common laboratory quality metrics. Resolving the differences between these two approaches is a critical global issue in analytical science.

Keywords:
AccuracyAllowable total error (ATE)BiasMeasurement uncertainty (MU)Total analytical error (TAE)

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Area of Science:

  • Clinical chemistry
  • Metrology
  • Laboratory medicine

Background:

  • Laboratory test quality is commonly assessed using Total Analytical Error (TAE), encompassing imprecision and bias.
  • The metrologic approach utilizes Measurement Uncertainty (MU), which operates under the assumption that bias is negligible or corrected.

Purpose of the Study:

  • To highlight the discrepancies between TAE and MU as measures of analytical quality.
  • To address the ongoing global challenge of reconciling these two distinct concepts in laboratory diagnostics.

Main Methods:

  • Comparative analysis of TAE and MU methodologies.
  • Literature review on current practices and theoretical frameworks.

Main Results:

  • TAE provides a comprehensive error estimate including bias.
  • MU focuses on uncertainty when bias is assumed to be absent or corrected.

Conclusions:

  • The divergence between TAE and MU presents a significant challenge in standardizing laboratory quality assessment.
  • Further research and consensus-building are needed to resolve these differences for accurate laboratory diagnostics.